Silicon nanophotonic networks for quantum optical information processing

Edwin E. Hach III

doi:10.1117/12.2229051

12 May 2016 Silicon nanophotonic networks for quantum optical information processing

Edwin E. Hach III

Proceedings Volume 9850, Machine Intelligence and Bio-inspired Computation: Theory and Applications X; 98500D (2016) https://doi.org/10.1117/12.2229051
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Silicon nanophotonics show a lot of promise as the basic architecture for quantum information processing devices. This is particularly the case in relation to the scalability of such devices. During this talk I will review our simple theoretical model of a structure that we have identified as a ‘fundamental circuit element’ for linear optical quantum information processing in silicon nanophotonics. In particular, we have shown that, owing to an effect we call Passive Quantum Optical Feedback (PQOF), the topology of this circuit element allows for certain possible operational advantages, in addition to inherent scalability, not expected in bulk linear optics. I will emphasize the extension of our work to larger networks, including the Knill-Laflamme-Milburn (KLM) Controlled-Not (CNOT) gate and its important constituent, the so-called Nonlinear Sign (NS) shifter. Further, I will discuss our ongoing effort to design and optimize scalable networks that seem to have useful applications in quantum metrology and sensing. In developing the discussion, I will examine recent developments related to incorporation of losses and spectral properties in such a way as to generalize our simple, continuous-wave (cw) model of essentially lossless operation. I will also discuss on-chip generation and control of entangled photons within the nanophotonic material itself, especially as related to potentially useful applications in information processing.

Citation Download Citation

Edwin E. Hach III "Silicon nanophotonic networks for quantum optical information processing", Proc. SPIE 9850, Machine Intelligence and Bio-inspired Computation: Theory and Applications X, 98500D (12 May 2016); https://doi.org/10.1117/12.2229051

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